Customer identification system and method

ABSTRACT

A method and system are presented for identifying a customer in a commercial transaction using less than complete identifying information. A name for the customer is extracted from a credit card during a purchase transaction. A trade area for the point of sale location is used to restrict a search of a demographic database to find a list of potential identity matches having names similar to the name on the credit card. A best match generator creates a profile of the expected purchaser of the products in the transaction. Using demographic information about each identity in the list of potential identity matches, each identity is compared to the profile and given a score. The highest scoring identity is then considered the best match. The best match identity is then assumed to be the identity involved in the transaction, and the customer database is updated to reflect this determination

PRIORITY

This application is a divisional application of U.S. patent applicationSer. No. 09/970,236, filed on Oct. 2, 2001.

FIELD OF THE INVENTION

The present invention relates generally to customer tracking in a retailenvironment. More particularly, the present invention relates to theutilization of a customer name and a trade area for a retail store toidentify a customer in a transaction and update a customer database.

BACKGROUND OF THE INVENTION

Almost every retailer has recognized the need to track the purchasesmade by customers in order to understand the desires and trends of itscustomers. Generally, such information is stored in one or morecomputerized databases. These databases are able to track purchases madethroughout a retail chain, and can be broken down according to a varietyof parameters, such as by product, region, store, or department. Oncethe database of purchases has been created, it can be used forforecasting, inventory management, and for promotional planning.

Many retailers have understood that these types of databases can be evenmore useful if they are able to track the habits of individualcustomers. If this can be done accurately, it would be possible toimprove forecasting and to perform complex demographic research on theircustomers. In addition, customer specific purchase information wouldallow the retailer to greatly improve the efficiency of its marketingand promotional planning, and would even allow direct one-on-onemarketing according to a customer's individual tastes, as determined bythe customer's previous purchases and general buying habits.

Unfortunately, it can be difficult to properly associate a particularpurchase at a retailer with a particular customer. Some means must beemployed to identify the customer at the point of sale. For instance, astore representative may ask the customer for their phone number. Thisinformation is then entered into a device at the point of sale, and thencompared with the existing database of customers. The comparisondetermines whether the customer already exists in the database. If anentry for that customer does exist in the database, the purchase thenbeing made is added to the database entry for an existing customer.Otherwise, a new customer entry is added to the database.

Various methods can be used to identify customers at the point of sale,with each method creating a different degree of confidence that thecustomer has been successfully identified. The most accurate method maybe to request identifying information such as a phone number directlyfrom the customer. This information can then be compared with thedatabase. If similar or identical information is retrieved, the customerwill be requested to verify that the information in the database isaccurate. If the match in the database is for a different individual, anew record in the database is created for that individual. Sometimes,multiple entries in the database might match the information receivedfrom the customer. In these circumstances, the customer can be directlyasked to select the appropriate entry, and, if necessary, multipleentries in the database for the same individual can be merged together.

Unfortunately, requesting such detailed, identifying information fromthe customer at the point of sale has several negative effects. First,the customer is often annoyed at the perceived invasion into theirprivacy. Second, requesting such information slows down the saletransaction, which decreases the efficiency for the store and increasescustomer frustration with any delay. Finally, customers may choose toprovide inaccurate information to indicate their displeasure at thesystem, which has obvious implications to the usefulness of thedatabase.

As a result, several companies have provided a service to retailers thatidentify a customer according to the credit card number used by thecustomer at the point of sale. The system simply looks the number up ina reverse listing of identities and credit card numbers provided bycredit card issuers. Using this system, all credit card purchases can beassociated with a particular individual, without any of the adverseconsequences described above. In addition, because each credit cardnumber is uniquely assigned to one individual or household, the returnedidentifying information has a high degree of accuracy.

Unfortunately, recent statutory changes in the United States has madethe provision of identifying information from credit card numbersdifficult. As a result, retailers are now searching for a way toautomatically identify customers at the point of sale without requiringthe customer to directly identify themselves.

SUMMARY OF THE INVENTION

The present invention meets this need by providing a system and methodthat identifies customers utilizing a credit card at the point of salewithout using the credit card number for identification purposes.Rather, the current system utilizes the name embedded on the credit cardas an initial identifier.

During a credit card transaction at a point of sale, the name and creditcard number are usually read off of the magnetic strip found on the backof the credit card. In the present invention, the name is searchedagainst a comprehensive demographic database in order to develop a listof potential matches. The list of potential matches is created byidentifying entries in the database that have a similar name as thatfound on the credit card, and also have a residential address within thetrade area for the retail store. The trade area is determined by astatistical analysis of customer purchase patterns on a store-by-storebasis.

Once the list of potential matches is created, the list is compared tothe products purchased with this credit card. The present invention thenapplies business rules to determine the most likely match between theactual customer making the purchase and an entry on the list ofpotential matches. These business rules can be embodied in a variety oftests applied to each of these potential matches. One test might examinethe strength of the name match, while another will identify whether apotential match is an existing customer that has purchased similarmerchandise in the past. Some of the more sophisticated tests develop ademographic profile based on the items purchased with the credit card,and then compare the characteristics of the potential matches againstthis profile. There is no guarantee that the system will select thecorrect match. It is clear, however, that the odds of determining thecorrect match are improved by greater sophistication in the businessrules used to examine the list of potential matches.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart showing the context in which the invention isutilized

FIG. 2 is a box diagram showing the basic components of the presentinvention.

FIG. 3 is a table showing data used to determine the trade area for apoint of sale location according to one embodiment of the invention.

FIG. 4 is a flow chart showing the detailed process of developing a listof potential matches.

FIG. 5 is a flow chart showing the detailed process of identifying abest match from the list of potential matches created in FIG. 4.

FIG. 6 is a table showing a profile for a particular luxury itemcomprised of a variety of tests.

FIG. 7 is a table showing additional tests.

FIG. 8 is a flow chart showing the integration of the best match fromFIG. 5 into the customer database.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is able to specifically identify customers incredit card or debit card transactions without using the card number asthe identifier. This invention works equally well with credit cards,debit cards, or any other payment mechanism that utilizes a uniqueidentifier to identify a payment account and associates that uniqueidentifier with a non-unique identifier, such as name.

For ease in understanding this application, the ability to identify acustomer will be described in the context of a credit card transaction.The scope of the present invention is not so limited, however, as it canbe utilized in any situation where the name of the customer can beidentified. In fact, the only unique aspect of the name is that it is apartial identifier, in that a name alone will not uniquely identify acustomer, but rather provides only a strong hint as to the identity ofthe customer.

The broad context in which the present invention is used is shown in theflowchart of FIG. 1. During a credit card purchase, the card will beswiped through a card reader that reads the magnetic strip found on theback of the card. The strip generally encodes the credit card number andthe name of the credit card holder. The reading of this information isshown as step 2 in FIG. 1.

Once this information is obtained, it is possible to update a customerdatabase with the details of the purchase transaction, as shown in step4. Traditionally, the items purchased, the store where purchased, andthe day and time of the purchase are stored in the customer databasealong with the credit card number. Since the purchases are stored withthe credit card, all purchases made with the same credit card can belinked with each other for later analysis.

While this is a useful way to analyze purchase data, it would bepreferred to associate purchases with a specific person or identity,rather than a specific credit card. Thus, the preferred customerdatabase will also contain identity records, through which individualsare associated with particular credit card numbers. In this way,purchases can be associated with individuals as well as credit cardnumbers.

Step 6 in FIG. 1 determines whether any identity record is associatedwith the credit card number used in the current transaction. This isaccomplished by examining the purchase database itself, and does notrequire the use of an external, reverse look-up table provided by creditcard companies. If an identity is already related to the card number,then there is no need to do any additional research to determine theindividual using this credit card. The information stored in step 4 issimply linked to the identified individual associated with this cardnumber. As a result, the process will terminate at step 8.

If the customer database has no record of any individual beingassociated with the card number being used, then it is necessary toidentify the user of the credit card and update the customer databaseaccordingly. This is accomplished by the present invention 10, which isset forth in the box diagram in FIG. 2.

As shown in this Figure, the present invention 10 utilizes informationobtained from the point of sale (or “POS”) location 20 in order toidentify the user of the credit card. Particularly, the presentinvention utilizes the purchase data 22, the name 24 found on the card,and an identifier 26 for the POS location 20 for the purpose ofidentifying the individual purchasing the items at POS 20. The number ofthe credit card, while used at the POS location 20 to complete thepurchase transaction, is not used by the present invention 10 toidentify the customer.

A point of sale location 20 will generally be a physical store location,although it is possible to use virtual stores as POS locations 20. Eachpoint of sale location 20 combines the purchase data 22, name 24, andPOS ID 26 into POS information 28, and then transmits this information28 to a centrally located potential match list generator 30.

The potential match list generator 30 is an application running on ageneral purpose computer. The match list generator 30 takes the POSinformation 28 from a variety of point of sale locations 20, andanalyzes the information 28 utilizing three databases 32, 34, and 36.The first database 32 is a broad, demographic database that containscontact information and other demographic information for the relevantgeographic area. In the preferred embodiment, the demographic database32 is a nationwide database containing household and individualinformation from across the country. Depending on the context in whichthe present invention is utilized, the demographic database 32 couldcontain information from a smaller or larger geographic area. Inaddition, while the demographic database 32 is shown in FIG. 2 as asingle database, it would be a simple alteration to utilize multipledemographic databases in the present invention instead of a singledatabase.

The second database 34 is a customer database containing purchase datafor the point of sale location 20 as well as other stores that aremanaged by the system 10. This customer database 34 is the same databasethat the entire system 10 is designed to maintain and update.

The third database 36 is a trade area database that contains informationabout past sales transactions at each of the point of sale locations 20that will be analyzed by the match list generator 30. The trade areadatabase 36 is utilized to identify the trade area, or “shopping area,”for each point of sale location 20, as identified by the POS ID 26. Thisarea is defined as the primary geographic region from which the storepulls shoppers. In defining this region, it is possible to use postalzip codes, telephone area codes or exchanges, municipality boundaries,or any other geographically pre-determined boundaries. The trade area isdetermined by statistically examining the past purchase history at thestore. Ideally, trade areas will be determined using only recent data,which will lead to more current results and allow the trade area foreach store to fluctuate according to the actual buying patterns ofcustomers at the store.

FIG. 3 shows a table 60 that can be used to help determine a trade areafor a POS location 20. The table 60 has six columns. The first column 62identifies the POS ID 26 while the second column 64 identifies the zipcodes in which customers of this POS location 20 reside. The thirdcolumn 66 identifies the number of recent sales made at the identifiedPOS location 20 to customers that reside in the identified zip code.This data is obtained from the purchase history for each POS location20, which could include data generated by the present invention 10 aswell as data generated by other means.

These first three columns 62, 64, 66 allow the table 60 to be used toanalyze the geographic spread of the customers that shop at the storeidentified by the POS ID 26 of column 62. This analysis takes place inthe next three columns 68, 70, and 72. Column 68 identifies thepercentage of total transactions at the identified store that took placeto customers within the zip code. When the rows are sorted according tocolumn 66 or 68, column 70 can then present a useful, cumulativepercentage of sales. It is then possible to set a cut-off threshold thatdetermines the trade area for the store. In FIG. 3, the cut-offthreshold is set at ninety percent, and is represented by a double line74. Those zip codes above the line 74 make up ninety percent of therecent sales to the store, and form the store's trade area. Those zipcodes below the line 74 are outside of the store's trade area. Byexamining column 72, it is clear that the zip codes are not chosenmerely by geographic proximity to the store, since some zip codesoutside of the trade area are closer to the store than zip codes withinthe trade area.

The use of trade areas is preferred over a strict geographic proximitytest (such as all households within 20 miles of the store location)since a trade area is a much more accurate predictor of the householdsthat actually shop within a store. A strict geographic area will notallow for distinguishing between a rural store that pulls from a largerarea than an urban store. In addition, even if the strict geographicarea is sensitive to the population density (i.e., 5 miles for an urbanstore, 30 miles for a rural store), the trade area technique allows forcomplex geographic areas to be created for each store according toactual shopping patterns in the area. In addition, trade areas definedby shopping patterns can be constantly redefined simply by re-analyzingrecent purchase data at regular intervals, and thus reflect changingpurchase habits.

Of course, the table 60 shown in FIG. 3 is just one method that could beused to generate a trade area for a particular point of sale location20. Other techniques to generate the trade area could be utilized andstill be within the scope of the present invention, so long as the tradearea is determined by using actual sales history from the store ratherthan mere geographic proximity.

Returning to FIG. 2, the potential match list generator 30 uses thestore ID 26 and the trade area database 36 to determine a trade area forthat store. Alternatively, the trade areas for all POS locations 20could be predetermined, with only the resulting trade area being storedin the trade area database 36. Either way, the match list generator thensearches the demographic database 32 and the customer database 34 forindividuals residing in the trade area having a name that is similar tothe name 24 taken from the credit card. These databases 32, 34 can useadvanced fuzzy logic and synonym search techniques in order to increasethe quality of the matches. Such techniques are well known in the priorart, and allow a search of nicknames, abbreviations, and other knownvariations that previously prevented high quality name matching.

The match list generator 30 then takes this information and creates alist of potential matches 38. Because multiple databases 32, 34 can beused as the source of the potential match list 38, the match listgenerator 30 must be sure to pare out any duplicate listings in the list38. After this is accomplished, the list 38 will contain zero, one, ormany potential matches for the name found on the credit card at the POSlocation 20. Since it is possible for more than one name to be found onlist 38, this list 38 is presented to a best match generator 40 in orderto determine a best match 46. Like the potential match list generator30, the best match generator 40 is an application program running on ageneral-purpose computer. The best match generator 40 can be run on thesame computer running the potential match list generator 30. In fact,both generators 30, 40 could even form separate subprocesses of the sameapplication program.

The best match generator 40 utilizes two databases 42 and 34, as well asthe purchase data 22 provided from the point of sale 20 to analyze thelist of potential matches 38 and generate the best match 46. The firstdatabase 42 is a demographic database, which may be the same demographicdatabase 32 used in connection with generating the list of potentialmatches. The important aspect of the demographic data shown as element42 is that it can be used to determine demographic information for theidentities found on the list of potential matches 38, such as familyincome, and the age and gender of all individuals living in a household.

The second database 34 is the main customer database that was also usedto help create the list of potential matches 38. The customer database34 is used by the best match generator 40 to analyze past purchasebehavior of each of the identities found in the list of potentialmatches 38.

The best match generator 40 uses these databases 42 and 34 to determinewhich identity in the list of potential matches was the most likelypurchaser of the products identified in the purchase data 22. Generally,this is accomplished by creating a profile of the likely purchaser ofthe products, and comparing that profile against what is known abouteach of the identities on this list 38. For example, if the purchasedata 22 indicates that the item purchased was an expensive, luxury item,the profile created for that product would indicate that the likelypurchaser probably has a large income. The demographic database 42 wouldthen be utilized to examine the comparative incomes of the identities onthe list 38 to help determine the best match 46.

In addition, the present invention might look at the confidence qualityof each name in the list of potential matches 38. For instance, the nameon the credit card might be “Richard M. Nixon.” Two individuals withsimilar names might reside within the trade zone of the store in whichthe purchase is made, specifically an individual known only as “DickNixon” and another individual who goes by the full name “Richard M.Nixon.” In this circumstance, the confidence of the second name on thelist would be higher than the first. As a result, the second name wouldscore higher on the match quality test.

In the preferred embodiment, different tests are applied and separatelyscored, and a total score is created for each identity on the list 38.The identity with the highest score is then chosen as the best match 46.The details of this type of scoring are set forth below in connectionwith FIGS. 5, 6, and 7.

The best match 46 is then used by a third software module known as arecord integrator 50 to update the customer database 34 with the contactinformation. Much like the best match generator 40 and the match listgenerator 30, the record integrator 50 operates on a general purposecomputer, and can form a separate program or form part of a singleapplication having the features of the other programs 30, 40. Thedetails of the process used by the record integrator 50 is shown belowin connection with the flow chart of FIG. 8.

FIG. 4 contains a flow chart 80 showing the process used by the currentinvention to generate a list of best matches. The first step 82 is toextract the name 24 from the magnetic stripe on a credit card. This is arelatively straightforward process, and is commonly accomplished bycredit card readers. The purpose of this step is to obtain the name 24of the purchaser so that it can be associated with the purchasetransaction, as explained above. Of course, the present invention is notlimited to this method of obtaining a name. For instance, it would alsobe possible to obtain the name off the card through another interface,such as a scannable optical code, or by hand entering the name off theface of the credit card. In addition, this process can work intransactions other than credit cards, such as in debit cardtransactions. It would even be possible to take a name from a check,however it is unlikely that the present invention would be necessary forchecking transactions. This is because it is now standard procedure toobtain a driver's license number for every check over a threshold amountreceived at a store. This driver's license number can then be used toaccurately obtain a specific identity for the purchaser.

Once the name 24 is obtained, it is combined with the POS ID 26 andpurchase data 22 and forwarded to the potential match list generator 30,in step 84. The generator 30 then utilizes the trade area database 36 toobtain an appropriate trade area in step 86. As explained above, thetrade area in the preferred embodiment is determined by examining recentpurchase data for the POS ID 26 and determining the geographic area fromwhich customers for that store generally originate.

In an alternative embodiment, the trade area is determined not only byexamining the POS ID 26, but also by examining all other transactions inthe customer database 34 that have been associated with the credit cardnumber. As explained above in connection with FIG. 1, all credit cardtransactions are stored in the customer database 34 and associated witha credit card number, even if there is no known association between thatnumber and an actual individual. As a result, it is possible that thereare numerous transactions associated with a credit card number beforeany association with an actual identity is made. By examining all ofthese transactions, it is possible to expand the trade area to cover allstores for which this credit card has been used to make purchases.Alternatively, the trade area could cover only those stores for which aset number of transactions have taken place.

It would even be possible to use the prior transactions for a creditcard number to narrow a trade area. One way of doing this is byexamining the trade areas for all of the stores for which at least two(or some other number) purchases have been made using the credit cardnumber. The trade area used to make the list of potential matches 38could be narrowed to include only those areas in which the trade areasof the separate stores overlap.

Regardless of how the trade area is defined in step 86, the process 80will search the demographic database for identities having names thatare similar to the name 24 in the POS information 28 (step 88), andwhich also are associated with an address found within the trade area(step 90). Although these two steps 88, 90 are shown as separate stepsin FIG. 4, they will most likely form part of a single database queryand hence be accomplished together. If they are kept separate, thesesteps 88, 90 can be accomplished as set forth in FIG. 4, or in theopposite order. Since it is possible that each of the identities in thedemographic database 32 will have multiple addresses, such as home,work, or vacation, step 90 may require that the identity be associatedwith only one address in the trade area.

Preferably, step 92 then examines the list of identities found throughsteps 88 and 90, and assigns a quality to the match. In this way, itwill be possible to distinguish between an exact name match and a matchmade through fuzzy logic and synonym searching capabilities. Once thisis accomplished, the list of potential matches 38 is complete, andprocessing is passed to process 100 which determines the best match.

The best match process 100 is shown in FIG. 5. This process 100 startswith a test at step 102 to determine whether or not the list ofpotential matches 38 contains any identities at all. If not, the bestmatch 46 is simply left empty in step 104, and processing will continuewith the update database process 180. If there are identities on list38, step 106 will determine if multiple identities exist. If not, thebest match 46 is set to the single identity in list 38 (step 108), andprocess 180 is started.

If there are multiple entries on list 38, then it is necessary toanalyze the list 38 to determine the best match 46. The first step 110in doing so is to generate a purchaser profile for the purchasedproducts. The purchaser profile is generated using historical purchasedata as well as marketing data for the products. For instance, thepurchase data may indicate that the sole product purchased was ahigh-end luxury item, such as a plasma, flat-panel television or a$3,000 silk suit. Market research and historical purchase data mayindicate that the most likely purchaser for such a luxury item is amarried individual with older or no children, aged 46-60, with at leasta college level education and who makes between $150,000 and $225,000per year. This example profile contains five characteristics: maritalstatus, children in household, age, education level, and income. Aprofile could have more or fewer characteristics, as is appropriategiven the quality of the market research and data analysis that givesrise to the profile.

Rather than merely generating one preferred value for eachcharacteristic in a profile, the preferred embodiment turns the profileinto a grouping of tests that reflects the importance of eachcharacteristic, and allows the profile to consider multiple values asrelevant within a given characteristic. FIG. 6 shows such a profile 150for the luxury item discussed in the previous paragraph.

As seen in FIG. 6, profile 150 has five subtests 152, one for each ofthe characteristics in the profile. Each subtest 152 contains at leasttwo possible values 154 for the associated characteristic, and providesa score 156 for each of the possible values 154.

A profile 150 like the one in FIG. 6 is generated for the product orproducts being purchased as identified in the purchase data 22.Generally, there will not be enough quality data to generate a profilefor each separate item being sold at the POS location 20, so profileswill be generated according to product type. Example product types mightinclude kitchen appliances, luxury/designer clothing items, children'svideos, hunting equipment, health and beauty items, or computers.Obviously, these categories can range from extremely broad (i.e.,clothing or food) to so narrow as to be product specific. The mostimportant factor in determining the breadth of the category is theaccuracy with which the profile 150 can be created.

It is possible to utilize the present invention by profiling only asingle item being purchased during a transaction, even if multiple itemsare actually found in the purchase data 22. It would be important toselect the item to be profiled carefully so as to most accurately selectthe best match 46 from the list of potential matches 38. One way ofdoing this is to select the most expensive item or category found in thepurchase data, or to select the item or category that was purchased inthe greatest quantity. In the preferred embodiment, multiple items areused to generate the profile. This can be accomplished by usingstatistics to analyze the items as a group in order to create a singletest for each characteristic. With numerous items, this type ofstatistical analysis could be difficult. Alternatively, each item can beseparately profiled, and the multiple profiles can be combined with eachscore for each attribute value being summed together. Either way, aprofile is created that matches most or all of the items purchased asshown in the purchase data.

In the preferred embodiment, the profile created in step 110 is createdspecifically for the products found in the purchase data 22 for thecurrent transaction. In an alternate embodiment, however, it would bepossible to examine all sales transactions in the customer database 34for the current credit card, which would allow the creation of a profile150 that represents all items ever purchased by the credit card.

In addition to the profile 150, it is also possible to create tests thatare not based upon the demographic information available in demographicdatabase 42. Two such tests are shown in the additional test set 160 ofFIG. 7. The first test VI in test set 160 utilizes data found in thecustomer database 34. In this test, a higher score is generated when theselected identity is found in the customer database 34. This reflects abelief that a previous customer is more likely to be a match than a new,previously unknown customer is. The test VI also generates additionalpoints if the customer database 34 shows that the customer haspreviously made purchases of the same category of goods as those foundin purchase data 22. Like the categories that can be used to create theprofile 150 described above, the categories used to evaluate test VI canbe defined broadly or narrowly, depending upon the ability to createstatistical relevant distinctions between classes.

Additional test set 160 also shows test VII, which is used to evaluatethe strength of the name match made by potential match list generator30. If the match were perfect, a higher score is generated than if thematch were less than perfect.

Returning to FIG. 5, the profile 150 is generated in step 110. In step112, a single identity from the list of potential matches 38 isselected. After the identity is selected, it is necessary to obtaindemographic information about that identity, such as age and income.This is done in step 114, and can be accomplished by consulting thedemographic database 42. Alternatively, since the same or similardatabase 32 could be used generate the list of potential matches 38, itis possible for the potential match list generator 30 to include theseattribute values within the list of potential matches 38. In step 116,the demographic information for the selected identity is then comparedto one of the tests in profile 150 and test set 160 in order to generatea test score.

As an example, the demographic database 42 may indicate in step 114 thatthe first identity is a 35 year old single woman, with no children, whohas a college degree and an income of $65,000. This woman does not showup in the customer database 34, and the name match is less than perfect.The first test in profile 150 is run in step 116, which generates ascore of 6 given her age of 35 years old. After this test is run in step116, step 118 determines if there are any more tests in the profile. Ifso, processing returns to step 116 until test scores have been generatedfor all of the tests. At that point, step 120 generates a total scorefor the identity. The example 35 year old woman would have a total scoreof 22 (6 for age 35, 6 for an income of $65,000, 0 for being single, 5for being a college graduate, 5 for having no children at home, 0 fornot appearing in the customer database, and 0 for a less than perfectname match).

Once the total score for an identity is created in step 120, the bestmatch process 100 determines whether any more identities need to beevaluated in step 122. If more identities remain, the process returns tostep 112 and the next identity is selected and scored. When step 122determines that all of the identities have been scored, step 124 selectsthe best match 46 by selecting the top scoring identity in the list ofpotential matches 38.

In one embodiment of the present invention, step 124 requires thehighest scoring identity to have a minimum score before selecting thatidentity as the best match 46. If the highest scoring identity does notmeet this minimum score, the best match 46 will be left empty. In thisembodiment, it would also be necessary to develop a test score evenwhere only a single identity was found in the list of potential matches38. Thus, step 108 in FIG. 5 would have to include the development of atest score and the comparison of this score against the allowed minimumscore.

Whether or not a minimum test score is required, processing continueswith the update database process 180 shown in FIG. 8. The first step 182of this process is to determine whether the best match 46 contains anidentity or is left empty. If it is empty, all processing isdiscontinued at 184, and the credit card number remains unassociatedwith an identity in the customer database 34.

If the best match 46 value contains an identity, step 186 determineswhether the identity matches an identity already in the customerdatabase 34. If not, step 188 creates a new identity using theinformation found in demographic database 42. This new identity is thenmatched with the credit card number so that future uses of this creditcard will be automatically associated with this identity. Processingthen ends at step 184.

If step 186 determines that the best match identity 46 is already in thecustomer database 34, then step 190 will update this identity bymatching it with the credit card number used in the latest transaction.By doing so, step 190 not only associates the identity with thepurchases made in the latest transaction, but also with all otherpurchases made by this same credit card. Step 190 can also update theidentity with other information associated with this identity in thedemographic database 42. After this updating, processing stops at step184.

The update database process 180 could also be responsible for mergingmultiple identities into one identity where it is found that what hadbeen thought to be two or more people is really one and the samecustomer. In addition, the update database process 180 could beresponsible for checking Nixie files of undeliverable addresses toupdate the address for the identity, and could also be responsible forremoving records for people known to be deceased. These additionalresponsibilities of the update database process are well-known in theart, and therefore are not specifically shown in the flow chart of FIG.8.

Of course, many possible combinations of features and elements arepossible within the scope of the present invention. For instance, whilethe present invention was described primarily in the context of a creditcard transaction, the invention is equally useful in debit cardsituations and any other situation where a customer name or anotherpartial identifier is known but an exact identity cannot be determined.

In addition, the present invention is shown as part of a process in FIG.1 where transaction data is saved to a customer database according to acredit card number even before an identity is clearly associated withthe transaction. This is not a pre-requisite for the present invention,and in fact the present invention can be used in any context where asales transaction needs to be associated with an identity but less thanperfect identifying information is known. In fact, the present inventioncould be used simply to validate already existing associations betweenidentities and credit card numbers.

The present invention is also described as having the potential matchlist generator 30, the best match generator 40, and the recordintegrator 50 as separate from the POS location 20 in order to handlemultiple POS locations 20. This is not a strict requirement of thepresent invention, and it would be possible to use the applicationprograms 30, 40, and 50 in the same physical location as POS location20.

It would also be possible to generate tests that are different thanthose set forth in FIGS. 6 and 7. For instance, one possible test mightprovide a score based on the distance in miles from the customer'saddress to the store where the purchase is made. This could be useful inembodiments where a trade area was not utilized to narrow the list ofpotential matches.

Finally, even though the demographic databases 32, 42 are shown assingle databases, it is possible and even likely that the information inthose databases will be found in multiple databases obtainable fromseparate, demographic database vendors. Because many such combinationsare present, the scope of the present invention is not to be limited tothe above description, but rather is to be limited only by the followingclaims.

1. A computer implemented method for identifying a customer in acustomer database system that tracks purchase transactions, the customerhaving purchased a product at a point of sale location by using apayment mechanism to pay for the product purchase, the methodcomprising: a) determining a customer name for the customer from thepayment mechanism; b) creating a computerized list of potential matches,the potential matches each being an identity of a particular person,wherein an identity constitutes information that uniquely identifies aparticular person, the list of potential matches being created by i)searching in a computerized database for identities having a namesimilar to the customer name, with similar names being determinedthrough at least one mechanism chosen from the set including identicallymatching names, nicknames, abbreviations, and known variations of thecustomer name, and ii) combining a plurality of the identities found bysearching the computerized database into the computerized list ofpotential matches; and c) selecting a best match identity from thecomputerized list of potential matches, the best match identityindicating which of the plurality of identities in the list of potentialmatches is to be selected as the identity for the customer name; and d)using the best match identity as the identity for the customer name inthe customer database system.
 2. The method of claim 1, wherein the stepof selecting a best match further comprises creating a score for eachidentity in the list of potential matches, and then selecting theidentity with the highest score as the best match identity.
 3. Themethod of claim 2, wherein the step of creating a score for eachidentity in the list of potential matches further comprising running aplurality of tests each having a separate score, and combining the scorefor all tests to create a score for the identity.
 4. The method of claim3, wherein one of the tests relates to whether the identity haspreviously purchased an item in the same category of goods as theproduct purchased at the point of sale location.
 5. The method of claim3, wherein one of the tests relates to whether the name of the identityin the list of potential matches is a perfect match for the customername determined from the payment mechanism.
 6. The method of claim 3,wherein one of the tests relates to the income of the identity in thelist of potential matches.
 7. The method of claim 3, wherein one of thetests relates to the education level of the identity in the list ofpotential matches.
 8. The method of claim 3, wherein one of the testsrelates to the age of the identity in the list of potential matches. 9.The method of claim 3, wherein one of the tests relates to the age ofchildren in the home of the identity in the list of potential matches.10. The method of claim 3, wherein the plurality of tests includes: i) afirst test that relates to whether the identity has previously purchasedan item in the same category of goods as the product purchased at thepoint of sale location, ii) a second test that relates to the income ofthe identity in the list of potential matches, and iii) a third testthat relates to the age of the identity in the list of potentialmatches.
 11. The method of claim 10, further comprising a fourth testthat relates to whether the name of the identity in the list ofpotential matches is a perfect match for the customer name determinedfrom the payment mechanism.
 12. The method of claim 1, wherein thecomputerized list of potential matches is created by searching in acomputerized list of prior customers who have made prior purchases. 13.The method of claim 12, further including the step of identifying priorpurchases made by each of the identities in the computerized list ofpotential matches, and further wherein the step of selecting a bestmatch identity further comprises comparing the product purchased at thepoint of sale location with the prior purchases made by each of theidentities in the computerized list of potential matches.
 14. The methodof claim 1 wherein the step of selecting a best match identity furthercomprising determining which identity in the list of potential matchesresides closest to the point of sale location.
 15. The method of claim1, wherein the step of selecting a best match identity furthercomprising determining which identity in the list of potential matcheshas a name that comes closest to the customer name determined from thepayment mechanism.
 16. The method of claim 1, wherein the step ofsearching in a computerized database for identities having a namesimilar to the customer name further comprises (1) creating a trade areafor the point of sale location, the trade area being determined throughan analysis of past sales at the point of sale location, the trade areadefining a geographic region containing the home addresses of a firstsubset of shoppers that have shopped at the point of sale location,while excluding a second subset of shoppers that have shopped at thepoint of sale location, and (2) searching for the identities beingassociated with an address within the trade area.
 17. A computerizedsystem for identifying a customer in a customer database system thattracks purchase transactions, the customer having purchased a product ata point of sale location by using a payment mechanism to pay for theproduct purchase, the system comprising: a) means for determining acustomer name for the customer from the payment mechanism; b) means forcreating a computerized list of potential matches, the potential matcheseach being an identity of a particular person, wherein an identityconstitutes information that uniquely identifies a particular person,the list of potential matches being created by i) searching in acomputerized database for identities having a name similar to thecustomer name, with similar names being determined through at least onemechanism chosen from the set including identically matching names,nicknames, abbreviations, and known variations of the customer name, andii) combining a plurality of the identities found by searching thecomputerized database into the computerized list of potential matches;and c) means for selecting a best match identity from the computerizedlist of potential matches, the best match identity indicating which ofthe plurality of identities in the list of potential matches is to beselected as the identity for the customer name; and d) means for usingthe best match identity as the identity for the customer name in thecustomer database system.